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NASA NTRS2022

VESIcal: An Open-source Thermodynamic Model Engine for Mixed Volatile (H2O-CO2) Solubility in Silicate Melts

Modeling the solubility of volatiles in silicate melts is fundamental to the interpretation of volcanic systems and has implications for magma dynamics, eruption style, and material transport between the mantle, crust, and atmosphere. Recent advancements in computational capabilities and access to computing tools has outpaced the functionality and extensibility of previously available modeling platforms. Here we present VESIcal (Volatile Equilibria and Saturation Index calculator), the first comprehensive modeling tool for H2O, CO2, and mixed (H2O-CO2) solubility in silicate melts that: a) allows users access to seven popular models, with easy inter-comparison between models; b) provides universal functionality for all models (e.g., functions for calculating saturation pressures, degassing paths, etc.); c) can process large datasets (1,000’s of samples) automatically; d) can output computed data into an Excel spreadsheet or CSV file for post-modeling analysis; e) integrates plotting capabilities directly within the tool; and f) provides all of this within the framework of a python library, making the tool extensible by the user and allowing any of the model functions to be incorporated into any other code capable of calling python.Here we will provide a demonstration of VESIcal and its capabilities with applications to various volcanic processes affected by volatiles. VESIcal represents the first tool capable of directly comparing multiple solubility models and equations of state. We find that commonly used models predict surprisingly different volatile solubilities, particularly for pure CO2 or mixed CO2-H2O fluids. Even for melt compositions that are well represented in the calibration datasets of multiple models (e.g., MORBs), calculated solubilities for pure CO2 and pure H2O can deviate from one another by factors of >2 leading to 2x deviations in calculated saturation pressures (e.g., 5 to 10 kbar). The solubility of CO2 predicted by different rhyolitic models also differs substantially, overwhelming other sources of uncertainty such as analytical errors on measurements of volatile contents or uncertainties in crustal density profiles. This highlights the importance of model choice when drawing geological conclusions based on volatiles in magmas.

Kayla Iacovino↗

NASA NTRS1978

Numerical simulation of warm fog and its application to warm fog prediction and modification

The considered theoretical model describes the evolution of potential temperature, water vapor content, liquid water content, and horizontal and vertical winds as determined by the processes of vertical turbulent transfer and horizontal advection for momentum, energy, and moisture, as well as radiation cooling, growth of water droplets based on microphysical processes, and drop sedimentation. The mathematical model is two-dimensional in the X-Z plane. The diffusivity coefficient is the same for liquid water droplets as for vapor. The fundamental equations governing the macrophysical processes of the evolution of wind components, water vapor content, liquid water content, and potential temperature under the influences of vertical turbulent diffusion transfer, turbulent momentum transfer, and turbulent energy transfer are expressed by three sets of conservation equations.

Hung, R. J.↗

NASA NTRS2005

Particle Size Distributions Measured in B757 Engine Plume During EXCAVATE

A ground-based test, the Experiment to Characterize Aircraft Volatile Aerosols and Trace Species Emissions (EXCAVATE), was conducted at NASA Langley Research Center, January 26 - 27, 2002, with a Boeing 757 aircraft. The aircraft was anchored on a tarmac and two probes were positioned downstream of the right-side engine, a Rolls Royce RB211-585. One probe was designed and fabricated by Arnold Engineering Development Center (AEDC) and had a 45.6 mm (1.794 in.) ID. A second probe, constructed of 6.4 mm (0.25 in.) stainless-steel tubing at NASA Langley Research Center, had a 6 mm (0.22 in.) ID. The engine was run on JP-5 with three different sulfur concentrations, 810 ppm, 1050 ppm, 1820 ppm; and was operated over a range of power settings from idle to near-full power. Particulate size-distributions and concentrations were measured at four downstream axial locations: 1 m and 10 m with the AEDC particulate probe, and 25 m and 35 m with the Langley probe. Fuel with various sulfur contents was tested to address the long-standing question of the role of sulfur in the formation of volatile species. Several experimental and modeling studies have shown a correlation between fuel sulfur-content and particulate-emissions. The object of EXCAVATE was to further study the effect of sulfur content on particulate number concentration and size-distribution as a function of location in the engine plume and engine operating conditions.

Sanders, Terry↗

NASA NTRS1994

The tropical water and energy cycles in a cumulus ensemble model. Part 1: Equilibrium climate

A cumulus ensemble model is used to study the tropical water and energy cycles and their role in the climate system. The model includes cloud dynamics, radiative processes, and microphysics that incorporate all important production and conversion processes among water vapor and five species of hydrometeors. Radiative transfer in clouds is parameterized based on cloud contents and size distributions of each bulk hydrometeor. Several model integrations have been carried out under a variety of imposed boundary and large-scale conditions. In Part 1 of this paper, the primary focus is on the water and heat budgets of the control experiment, which is designed to simulate the convective - radiative equilibrium response of the model to an imposed vertical velocity and a fixed sea surface temperature at 28 C. The simulated atmosphere is conditionally unstable below the freezing level and close to neutral above the freezing level. The equilibrium water budget shows that the total moisture source, M(sub s), which is contributed by surface evaporation (0.24 M(sub s)) and the large-scale advection (0.76 M(sub s)), all converts to mean surface precipitation bar-P(sub s). Most of M(sub s) is transported verticaly in convective regions where much of the condensate is generated and falls to surface (0.68 bar-P(sub s)). The remaining condensate detrains at a rate of 0.48 bar-P(sub s) and constitutes 65% of the source for stratiform clouds above the melting level. The upper-level stratiform cloud dissipates into clear environment at a rate of 0.14 bar-P(sub s), which is a significant moisture source comparable to the detrained water vapor (0.15 bar-P(sub s)) to the upper troposphere from convective clouds. In the lower troposphere, stratiform clouds evaporate at a rate of 0.41 bar-P(sub s), which is a more dominant moisture source than surface evaporation (0.22 bar-P(sub s)). The precipitation falling to the surface in the stratiform region is about 0.32 bar-P(sub s). The associated latent heating in the water cycle is the dominant source in the heat budget that generates a net upward motion in convective regions, upper stratiform regions (above the freezing level), and a downward motion in the lower stratiform regions. The budgets reveal a cycle of water and energy resulted from radiation-dynamic-convection interactions that maintain equilibrium of the atmosphere.

Sui, C. H.↗

DOE OSTI2025

Understanding the Transport Properties of Methane–Hydrogen Mixtures under the Interior Conditions of Ice Giants

Inaccuracy in our knowledge of the transport properties of relevant mixtures under planetary interior conditions is a roadblock in predicting the observable properties of planets in our solar system and beyond. In this work, we investigate methane–hydrogen mixtures using data sets obtained from density functional theory calculations for the electronic structure, combined with molecular dynamics simulations for the ions for a wide range of pressure and temperature. Hydrogen concentration significantly affects the equation of state (EOS) but has little influence on transport properties. We provide an analytical expression to model thermal EOS and transport properties as a function of hydrogen content, with the maximum deviation observed at low P–T conditions. These insights are particularly relevant to improve the planetary models and enhance our ability to predict the properties of “ice” giants and beyond.

Methane↗

NASA NTRS1994

Clouds, hazes, and the stratospheric methane abundance in Neptune

Analysis of high-spatial-resolution (approximately 0.8 arcsec) methane band and continuum imagery of Neptune's relatively homogeneous Equatorial Region yields significant constraints on (1) the stratospheric gaseous methane mixing ratio (f(sub CH4, S)), (2) the column abundances and optical properties of stratospheric and tropospheric hydrocarbon hazes, and (3) the wavelength-dependent single-scattering albedo of the 3-bar opaque cloud. From the center-to-limb behavior of the 7270-A and 8900-A CH4 bands, the stratospheric methane mixing ratios is limited to f(sub CH4, S) less than 1.7 x 10(exp -3), with a nominal value of f(sub CH4, S) = 3.5 x 10(exp -4), one to two orders of magnitude less than pre-Voyager estimates, but in agreement with a number of recent ultraviolet and thermal infrared measurements, and largely in agreement with the tropopause mixing ratio implied by Voyager temperature measurements. Upper limits to the stratospheric haze mass column abundance and 6190-A and 8900-A haze opacities are 0.61 micrograms/sq cm and 0.075 and 0.042, respectively, with nominal values of 0.20 micrograms/sq cm and 0.025 and 0.014 for the 0.2 micrometers radius particles preferred by the recent Voyager PPS analysis of Pryor et al. (1992). The tropospheric CH4 haze opacities are comparable to that found in the stratosphere, i.e., upper limits of 0.104 and 0.065 at 6190 A and 8900 A, respectively, with nominal values of 0.085 and 0.058. This indicates a column abundance less than 11.0 micrograms/sq cm, corresponding to the methane gas content within a well-mixed 3% methane tropospheric layer only 0.1 cm thick near the 1.5-bar CH4 condensation level. Conservative scattering is ruled out for the opaque cloud near 3 bars marking the bottom of the visible atmosphere. Specifically, we find cloud single-scattering albedos of 0.915 +/- 0.006 at 6340 A, 0.775 +/- 0.012 at 7490 A, and 0.803 +/- 0.010 at 8260 A. Global models utilizing a complete global spectrum confirm the red-absorbing character of the 3-bar cloud. The global-mean model has approximately 7.7 times greater stratospheric aerosol content than the Equatorial Region. An analysis of stratospheric haze precipitation rates indicates a steady-state haze production rate of 0.185-1.5 x 10(exp -14) g/sq cm/s, in agreement with recent theoretical photochemical estimates. Finally, reanalysis of the Voyager PPS 7500-A phase angle data utilizing the f(sub CH4, S) value derived here confirms the Pryor et al. result of a tropospheric CH4 haze opacity of a few tenths in the 22-30 degs S latitude region, several times that of the Equatorial Region or of the globe. The factor-of-10 reduction in f(sub CH4, S) below that assumed by Pryor et al. implies decreased gas absorption and consequently a decrease in the forward-scattering component of tropospheric aerosols.

Baines, Kevin H.↗

DOE OSTI2020

Dual frequency comb spectroscopy of solid fuel pyrolysis and combustion: Quantifying the influence of moisture content in Douglas fir

As the fire research community develops multi-scale models to predict the spread of wildland fires, there is a need for well-controlled, small-scale experiments which provide benchmark data and fundamental understanding of the effects of key parameters on the combustion of wildland fuels. Here, we implement a unique suite of non-intrusive, quantitative diagnostics that simultaneously measure mass loss, surface temperature, in situ gas temperature and water vapor concentration, including the first implementation of dual frequency comb spectroscopy for the study of solid fuels. As a demonstration, we examine the effect of fuel moisture content on the burning of Douglas fir wood samples that are ignited under constant radiative heat flux from a cone calorimeter to provide benchmark data for multi-scale models. Peak flame temperatures 3 mm above the sample surface measured with the dual frequency comb laser spectrometer are in excess of 1200 K. The flame temperature decreases by over 150 K as fuel moisture content increases from 0.7% to 40%. This work demonstrates a platform for simultaneous, quantitative measurements of well-controlled small-scale experiments which provide benchmark data for the development of advanced modeling and simulations capable of capturing the complex physics present in wildland fire systems.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1999

Radiative Forcing by Contrails

A parametric study of the instantaneous radiative impact of contrails is presented using three different radiative transfer models for a series of model atmospheres and cloud parameters. Contrails are treated as geometrically and optically thin plane parallel homogeneous cirrus layers in a static atmospheres The ice water content is varied as a function of ambient temperature. The model atmospheres include tropical, mid-latitude, and subarctic summer and winter atmospheres Optically thin contrails cause a positive net forcing at top of the atmosphere. At the surface the radiative forcing is negative during daytime. The forcing increases with the optical depth and the amount of contrail cover. At the top of the atmosphere a mean contrail cover of 0.1% with average optical depth of 0.2 to 0.5 causes about 0.01 to 0.03 W/m(exp 2)a daily mean instantaneous radiative forcing. Contrails cool the surface during the day and heat the surface during the night, and hence reduce the daily temperature amplitude The net effect depends strongly on the daily variation of contrail cloud cover. The indirect radiative forcing due to particle changes in natural cirrus clouds may be of the same magnitude as the direct one due to additional cover.

Meerkoetter, R.↗

NASA NTRS1987

A model El Nino-Southern Oscillation

A coupled atmosphere-ocean model is developed and used to study the ENSO (El Nino/Southern Oscillation) phenomenon. With no anomalous external forcing, the coupled model reproduces certain key features of the observed phenomenon, including the recurrence of warm events at irregular intervals with a preference for three to four years. It is shown that the mean sea surface temperature, wind and ocean current fields determine the characteristic spatial structure of ENSO anomalies. The tendency for phase-locking of anomalies is explained in terms of a variation in coupling strength associated with the annual cycle in the mean fields. Sensitivity studies reveal that both the amplitude and the time scale of the oscillation are sensitive to several parameters that affect the strength of the atmosphere-ocean coupling. Stronger coupling implies larger oscillations with a longer timescale. A critical element of the model oscillation is the variability in the equatorial heat content of the upper ocean. Equatorial heat content increases prior to warm events and decreases sharply during the events. A theory for this variability and the associated transitions between non-El Nino and El Nino states is presented. Implications of the model results for the prediction of El Nino events are discussed.

Zebiak, Stephen E.↗

NASA NTRS1990

Observation simulation experiments with regional prediction models

Research efforts in FY 1990 included studies employing regional scale numerical models as aids in evaluating potential contributions of specific satellite observing systems (current and future) to numerical prediction. One study involves Observing System Simulation Experiments (OSSEs) which mimic operational initialization/forecast cycles but incorporate simulated Advanced Microwave Sounding Unit (AMSU) radiances as input data. The objective of this and related studies is to anticipate the potential value of data from these satellite systems, and develop applications of remotely sensed data for the benefit of short range forecasts. Techniques are also being used that rely on numerical model-based synthetic satellite radiances to interpret the information content of various types of remotely sensed image and sounding products. With this approach, evolution of simulated channel radiance image features can be directly interpreted in terms of the atmospheric dynamical processes depicted by a model. Progress is being made in a study using the internal consistency of a regional prediction model to simplify the assessment of forced diabatic heating and moisture initialization in reducing model spinup times. Techniques for model initialization are being examined, with focus on implications for potential applications of remote microwave observations, including AMSU and Special Sensor Microwave Imager (SSM/I), in shortening model spinup time for regional prediction.

Diak, George↗

NASA NTRSDate not supplied

A Model-Based Systems Engineering Journey to Developing a Concept of Operations

Starting in 2017, NASA’s Human Research Program (HRP) Exploration Medical Capability (ExMC) element began a systems engineering transition from traditional, document-centric development to model-centric development when defining its foundation medical systems. These foundation medical systems define a Concept of Operations (ConOps) and identify the generic requirements for a medical system based on assumptions about a generic crew and mission environments and guidance from NASA standards (e.g., Medical “Levels of Care”). By making the transition, ExMC intends to improve communication among stakeholders about foundation medical system requirements and content. In addition, this transition will enable ExMC to lower both development and crew treatment risks for future, mission-specific medical systems. ExMC followed a Model Based Systems Engineering (MBSE) paradigm when developing the foundation medical systems. A model-based approach provides several advantages over a traditional, document-centric approach. First, when Systems Engineers (SE) develop diagrams in a model using a standard modeling language, they produce information dense pictures that facilitate understanding much more efficiently with less room for misinterpretation than text. Second, due to the evolving nature of projects, documentation becomes out of date the minute it is published. This can result in people making decisions based on information that is no longer current, especially if they are referencing a locally-stored copy of a document. A model, on the other hand, is always up to date with the latest approved changes and information. It serves as a single point of truth. Third, a model-centric approach centralizes all important information in one place. Rather than having to flip through separate ConOps documents, design specifications, requirements specifications, and the like to coordinate information, a model captures the content in one, integrated spot. This integration makes tracing information from end-to-end easier with greater reliability. The ExMC Systems Engineering Lifecycle follows a well-defined process. ExMC Systems Engineers perform all major steps of the process, regardless of the development methodology. One of the first steps in the process is developing the ConOps that describes the operation of the system from the point of view of the users. It includes a list of the users and their needs, the goals of the medical system, key assumptions about the system, and definitions of the medical system’s operational environments. For this development effort, ExMC chose to replace the traditional text-based ConOps document with a model. While the decision to change the development workflow was not difficult, implementing the structural and organizational workflows were. It required showing ExMC’s users, most of whom are not Systems Engineers, how the information they require would be presented in the model and to gain their acceptance of this approach. This paper documents key lessons learned during the ConOps transformation by focusing on how the model represents information, the agile workflow used by SEs when developing the model and how it integrates into a project plan, how leadership influenced key users to accept the transformation, and how the users interact with the model information.

Jeffrey Robert Cohen↗

DOE OSTI2024

A High-Performance Discrete-Element Framework for Simulating Flow and Jamming of Moisture Bearing Biomass Feedstocks

We developed and verified a high-performance open-source discrete element method (DEM) solver with simultaneously-supported feedstock-specific interaction models, including bonded-sphere, liquid bridge, cohesion, and non-linear contact models. Our solver uses parallel data structures on hybrid central and graphics processing unit (CPU/GPU) architectures, with favorable strong scaling performance observed for large problem sizes comprised of (100 M particles), and 4X single-node GPU speedup. The particles for corn stover feedstock were conceptualized and calibrated based on experimental measurements and results. Sensitivity analyses demonstrate that the mass flow rate from a wedge hopper is governed primarily by moisture content, friction coefficient, and cohesion energy density. The model is used to reproduce experimentally observed hopper jamming results, highlighting that the experimental no-flow trends can only be achieved by using non-spherical particles, liquid bridge and cohesion models, highlighting the importance of using concurrent feedstock specialized models for the effective representation of biomass material handling problems.

bioenergy↗

DOE OSTI2023

Explainable AI (XAI)-driven vibration sensing scheme for surface quality monitoring in a smart surface grinding process

Local Interpretable and Model-agnostic Explanation (LIME), an explainable artificial intelligence (XAI) approach is adapted to identify the globally important time-frequency bands for predicting average surface roughness (Ra) in a smart grinding process. The smart grinding setup consisted of a Supertech CNC precision surface grinding machine, instrumented with a Dytran piezoelectric accelerometer attached to the tailstock quill along the tangential direction (Y-axis). For every grinding pass, vibration signatures were captured, and the ground truth surface roughness values were recorded using a Mahr Marsurf M300C portable surface roughness profilometer. The roughness values ranged from 0.06 to 0.14 microns over the complete set of experiments. Time-frequency domain spectrogram frames were extracted for each of the vibration signals collected during the grinding process. Convolutional Neural Networks (CNNs) were modeled to predict the surface roughness based on these spectrogram frames and their image augmentations. The best CNN model was able to predict the roughness values with an overall R2-score of 0.95, training R2-score of 0.99, and testing R2-score of 0.81 with only 80 sets of vibration signals corresponding to 4 experiments with 20 trials each. Although the data size is not large enough to guarantee such performance metrics in real-world scenarios, one can extract statistically consistent explanations underlying the relationships these complex deep learning models capture. Further, the LIME methodology was implemented on the developed surface roughness CNN model to identify the important time-frequency bands (i.e., the superpixels of a spectrogram) influencing the predictions. Based on the identified important regions on the spectrogram frames, the corresponding frequency characteristics were determined that influence the surface roughness predictions. The important frequency range based on LIME results was approximately 11.7 to 19.1 kHz. The power of XAI was demonstrated by cutting down the sampling rate from 160 kHz to 30, 20, 10, and 5 kHz based on the important frequency range and considering Nyquist criteria. Separate CNN models were developed for these ranges by only extracting time-frequency contents below their corresponding Nyquist cut-offs. A proper data acquisition strategy is proposed by comparing the model performances to argue the selection of a sufficient sampling rate to capture the grinding process successfully and robustly.

42 ENGINEERING↗

DOE OSTI2023

Reprint of: Explainable AI (XAI)-driven vibration sensing scheme for surface quality monitoring in a smart surface grinding process

47 OTHER INSTRUMENTATION↗

NASA NTRS1991

Implanted 3He, 4He, and Xe in further studies of diamonds from Western Australia

In measurements of the noble gases in additional samples of diamonds from the Argyle and Ellendale lamproites in Western Australia we have failed to encounter any neon-rich stones such as showed solar-like isotopic compositions in earlier work. No neon was detected above the relatively high blank levels in our glass apparatus. White and brown diamonds showed no differences in noble gas content, nor did samples segregated by the color of long-wave UF fluorescence. The rare gas patterns in the 1.2 Ga Argyle pipe are largely consistent with implanted 3He, 4He, and fissiogenic Xe from U/Th in the matrix rock in which the diamonds have been stored for so long. These implanted species are absent in diamonds from the much younger (approximately 20 Ma) Ellendale pipe. We give implantation formulae for several models of inhomogeneously distributed U/Th. Differences in 3He content between pipe and alluvial Argyle samples are consistent with expected cosmogenic production in the latter. An expanded data base for helium and carbon isotopic data on the same samples supports a negative [4He]-delta 13C correlation seen earlier in work from our group, but if the Argyle samples, which contain light carbon, are corrected for implanted 4He, the correlation is considerably weakened. We no longer see an earlier 3He/4He-delta 13C correlation.

NASA Discipline Number 52-10↗

NASA NTRS1994

Automated Performance Prediction of Message Passing Parallel Programs

As the trend toward massively parallel processing continues, the need for tools that can predict scalability trends becomes greater. While high level languages Eke HPF have come into greater use, explicit message-passing programs proliferate, and will probably do so for some time, thanks to the onslaught of standards such as MPI. Much work has been done to create simple models that represent important characteristics of parallel programs, such as latency, network contention, and communication volume. But many of these methods still require a substantial manual effort to represent an application in the model's format. The YAPP ("Yet Another Performance Predictor") tool is an attempt to automate the formation of first-order expressions for completion time, with a minimum of programmer assistance. The content of this paper is as follows: First, we explore the implementation details of YAPP, and illustrate with examples some of the reasons that automatic prediction is difficult. In the following sections, we present the results of four applications, using execution traces on the Intel i860, analyze the error in YAPP's predictions, explain the limitations of our implementation, and mention possible future additions. In particular, we illustrate techniques used to identify pipeline communication patterns, and demonstrate how compiler analysis and regression are combined to automate the prediction process.

Block, Robert J.↗

DOE OSTI2021

Dark Energy Survey Year 3 Results: Constraints on cosmological parameters and galaxy bias models from galaxy clustering and galaxy-galaxy lensing using the redMaGiC sample

We constrain cosmological parameters and galaxy-bias parameters using the combination of galaxy clustering and galaxy-galaxy lensing measurements from the Dark Energy Survey Year-3 data. We describe our modeling framework and choice of scales analyzed, validating their robustness to theoretical uncertainties in small-scale clustering by analyzing simulated data. Using a linear galaxy bias model and redMaGiC galaxy sample, we obtain constraints on the matter content of the universe to be $\Omega_{\rm m} = 0.325^{+0.033}_{-0.034}$. We also implement a non-linear galaxy bias model to probe smaller scales that includes parameterizations based on hybrid perturbation theory, and find that it leads to a 17% gain in cosmological constraining power. Using the redMaGiC galaxy sample as foreground lens galaxies, we find the galaxy clustering and galaxy-galaxy lensing measurements to exhibit significant signals akin to decorrelation between galaxies and mass on large scales, which is not expected in any current models. This likely systematic measurement error biases our constraints on galaxy bias and the $S_8$ parameter. We find that a scale-, redshift- and sky-area-independent phenomenological decorrelation parameter can effectively capture this inconsistency between the galaxy clustering and galaxy-galaxy lensing. We perform robustness tests of our methodology pipeline and demonstrate stability of the constraints to changes in the theory model. After accounting for this decorrelation, we infer the constraints on the mean host halo mass of the redMaGiC galaxies from the large-scale bias constraints, finding the galaxies occupy halos of mass approximately $1.5 \times 10^{13} M_{\odot}/h$.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Steric Sea Level Rise and Relationships with Model Drift and Water Mass Representation in GFDL CM4 and ESM4

Abstract Density-driven steric seawater changes are a leading-order contributor to global mean sea level rise. However, intermodel differences in the magnitude and spatial patterns of steric sea level rise exist at regional scales and often emerge during the spinup and preindustrial control integrations of climate models. Steric sea level results from an eddy-permitting climate model, GFDL CM4, are compared with a lower-resolution counterpart, GFDL-ESM4. The results from both models are examined through basin-scale heat budgets and watermass analysis, and we compare the patterns of ocean heat uptake, redistribution, and sea level differ in ocean-only [i.e., Ocean Model Intercomparison Project (OMIP)] and coupled climate configurations. After correcting for model drift, both GFDL CM4 and GFDL-ESM4 simulate nearly equivalent ocean heat content change and global sea level rise during the historical period. However, the GFDL CM4 model exhibits as much as a 40% increase in surface ocean heat uptake in the Southern Ocean and subsequent increases in horizontal export to other ocean basins after bias correction. The results suggest regional differences in the processes governing Southern Ocean heat export, such as the formation of Antarctic Intermediate Water (AAIW), Subpolar Mode Water (SPMW), and gyre transport between the two models, and that sea level changes in these models cannot be fully bias-corrected. Since the process-level differences between the two models are evident in the preindustrial control simulations of both models, these results suggest that the control simulations are important for identifying and correcting sea level–related model biases.

Krasting, John P. [a NOAA/OAR/Geophysical Fluid Dy↗